Medical imaging is one of the most useful diagnostic tools available in modern medicine. Medical imaging allows medical personnel to non-intrusively look into a living body in order to detect and assess many types of injuries, diseases, conditions, etc. Medical imaging allows doctors and technicians to more easily and correctly make a diagnosis, decide on a treatment, prescribe medication, perform surgery or other treatments, and the like.
There are medical imaging processes of many types and for many different purposes, situations, or uses. They commonly share the ability to create an image of a bodily region of a patient, and can do so non-invasively. Examples of some common medical imaging types are positron emission tomography (PET), single photon emission computed tomography (SPECT), electron-beam X-ray computed tomography (CT), magnetic resonance imaging (MRI), and ultrasound (US).
Each imaging modality has specific benefits, limitations and uses and falls into a specific category such as nuclear medicine. For example x-rays are typically employed in imaging hard substances such as bone and teeth.
Nuclear medicine imaging employs a source of radioactivity to image a patient. Typically, a radiopharmaceutical is injected into the patient. Radiopharmaceutical compounds contain a radioisotope that undergoes gamma-ray decay at a predictable rate and characteristic energy. One or more radiation detectors are placed adjacent to the patient to monitor and record emitted radiation. Sometimes, the detector is rotated or indexed around the patient to monitor the emitted radiation from a plurality of directions. Based on information such as the detected position and energy, the radiopharmaceutical distribution in the body is determined and an image of the distribution is reconstructed to study the circulatory system, radiopharmaceutical uptake in selected organs or tissue, and the like.
Using these or other imaging types and associated machines, an image or series of images may be captured. Other devices may then be used to process the image in some fashion. Finally, as previously discussed, a doctor or technician may read the image in order to provide a diagnosis.
Certain applications of these imaging modalities require high-resolution images of a targeted field of view (FOV) that is less than the scan FOV for the imaging system. For example, in cardiac imaging, a high-resolution image of a small sub-region of the patient's anatomy may be desired. In emission tomography, e.g., PET or SPECT, the measured projection data contains activity from outside this targeted FOV. While reconstruction of this targeted FOV is generally straightforward for analytical reconstruction algorithms (such as filtered back projection), iterative reconstruction techniques typically require that the targeted FOV include the entire region of support of the image. This is because iterative reconstruction techniques attempt to match the estimated projection data (derived from forward projection of an estimated image) to the measured projection data. If the estimated projection data does not support the signal from outside the targeted FOV, the estimated projection data cannot correctly match the measured projection data.
In general, the signal from outside the targeted FOV should be accounted for in the image reconstruction. If the signal from outside the targeted FOV is not accounted for, the entire signal from outside the targeted FOV will be assigned to the periphery of the targeted FOV. However, this approach may result in a visible artifact at the periphery of the reconstructed image and quantitatively inaccurate regions throughout the reconstructed image.
In other cases, when a targeted FOV less than the scan FOV is requested, the full scan FOV may be reconstructed at high resolution. Subsequently, the image for the desired targeted FOV may be extracted from this image for the full scan FOV.
Specifically, in the iterative reconstruction technique, an estimate of the reconstructed volume of image data is forward projected onto the plane of the detector. The forward projected data is compared to the measured projection data. If the estimate of the reconstructed image were perfect, these two projections of data would match and there would be no difference. However, as the image is being built, there typically is a difference or error. The error or its inverse is then backprojected into the image volume to correct the volumetric image and create a new estimate for the next iteration.
More specifically, the iterative reconstruction process continues until the measured and forward projected data sets match within an acceptable error. If the iterative process is run for too long, it can start to degenerate the reconstructed image as well as being computationally expensive in terms of resource and time. One technique is to filter the measured data or at a point during a reconstruction or filter the reconstruction images. While such filtering helps to reduce noise in an image, it also reduces image resolution.
Ordered subsets are widely used for reducing the computational burden of iterative reconstruction algorithms. However, this approach should be used carefully, as the results are not proven to achieve the Maximum Likelihood estimate. Typically, Ordered Subset Expectation Maximization (OSEM) is used for image reconstruction. The reconstruction method includes resolution modeling, along with standard normalization, scatter, and attenuation corrections.
Using conventional subset selection in reconstruction algorithms produces noticeable artifacts in the transaxial views of the image. These artifacts can be strong enough to propagate to artifacts in the axial views of the images. Some artifacts have origins in the number of subsets used. By selecting a subset that results in a whole number factor of the block periodicity, an azimuthally periodic block structure artifact appears in the image.
Other types of image artifacts are independent of the selection of subset numbers used. One such artifact is the stretching of small size hot regions in the tangential direction with respect to the closest object boundary point in the transaxial view of the object. Another example is structuring of the noise in uniform background areas, which appear as rings.
Conventionally used subset algorithms utilize equidistant projections through the entire range of angles as shown in FIG. 1A. This scheme intuitively gives a maximum amount of azimuthal information per subset update. The algorithm updates the image through all of the subsets to obtain one iteration of the iterative process.
There remains a need in the art for improvement in image reconstruction techniques in order to increase accuracy and resolution.